Scholarlink

Summary <p>AI-derived bone mineral density from routine radiographs showed strong agreement with DXA and comparable ability to predict incident fractures. This opportunistic approach may support osteoporosis screening and early identification of high-risk individuals without reliance on dedicated DXA examinations.</p> Introduction <p>Osteoporosis is a major cause of fragility fractures, yet limited access to DXA leads to underdiagnosis and delayed treatment. Recent advances in artificial intelligence enable extraction of bone structural information from routine radiographs, providing a potential tool for opportunistic osteoporosis screening. Whether AI-derived BMD can approximate DXA and predict real-world fracture risk remains unclear.</p> Methods <p>Adults aged ≥ 20&#xa0;years who underwent both lumbar DXA and radiographic examinations (lumbosacral or kidney–ureter–bladder) within six months between January 2014 and December 2024 were retrospectively analyzed. Lumbar BMD was estimated using DeepXray Spina and compared with DXA using Pearson correlation, intraclass correlation coefficient (ICC), and Bland–Altman analysis. Diagnostic performance for osteoporosis (T-score ≤  − 2.5) and fracture prediction was evaluated using receiver operating characteristic (ROC) analysis, Cohen’s κ, and logistic regression.</p> Results <p>Among 540 participants (73.9% female; mean age 57.0&#xa0;years; mean follow-up 6.4&#xa0;years), AI- and DXA-derived BMD showed strong agreement (r = 0.943; ICC = 0.934). For osteoporosis diagnosis, AI-derived T-scores achieved an AUC of 0.959, κ = 0.74, and 90% accuracy. AI- and DXA-derived BMD showed comparable performance for predicting vertebral (AUC 0.704 vs. 0.678) and hip fractures (0.716 vs. 0.678). For all-site fractures, AI-derived BMD showed a modestly higher AUC than DXA-derived BMD (AUC, 0.699 vs 0.677; P = 0.042). Lower AI-derived BMD was independently associated with higher fracture risk.</p> Conclusions <p>AI-derived BMD from routine radiographs closely correlates with DXA and demonstrates comparable fracture prediction. This approach may support opportunistic osteoporosis screening without reliance on DXA.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-derived bone mineral density from standard radiographs compared with DXA for fracture prediction in a 10-year real-world cohort study

  • 期刊论文

  • 发表日期：

    2026年6月18日

查看全文
  • Tzu-Hao Tseng,
  • Tseng Ti Huang,
  • Jing En Huang,
  • Shi‑Chien Tzeng,
  • Yu-Chen Wang,
  • Pei-Chen Tsao,
  • Chih-Chien Hung,
  • Chia-Che Lee,
  • Jui-Yo Hsu,
  • Hung-Kuan Yen,
  • Chih-Hsing Wu,
  • Chung-Yi Li,
  • Chen-Yu Wang,
  • Shau-Huai Fu

摘要

Summary

AI-derived bone mineral density from routine radiographs showed strong agreement with DXA and comparable ability to predict incident fractures. This opportunistic approach may support osteoporosis screening and early identification of high-risk individuals without reliance on dedicated DXA examinations.

Introduction

Osteoporosis is a major cause of fragility fractures, yet limited access to DXA leads to underdiagnosis and delayed treatment. Recent advances in artificial intelligence enable extraction of bone structural information from routine radiographs, providing a potential tool for opportunistic osteoporosis screening. Whether AI-derived BMD can approximate DXA and predict real-world fracture risk remains unclear.

Methods

Adults aged ≥ 20 years who underwent both lumbar DXA and radiographic examinations (lumbosacral or kidney–ureter–bladder) within six months between January 2014 and December 2024 were retrospectively analyzed. Lumbar BMD was estimated using DeepXray Spina and compared with DXA using Pearson correlation, intraclass correlation coefficient (ICC), and Bland–Altman analysis. Diagnostic performance for osteoporosis (T-score ≤  − 2.5) and fracture prediction was evaluated using receiver operating characteristic (ROC) analysis, Cohen’s κ, and logistic regression.

Results

Among 540 participants (73.9% female; mean age 57.0 years; mean follow-up 6.4 years), AI- and DXA-derived BMD showed strong agreement (r = 0.943; ICC = 0.934). For osteoporosis diagnosis, AI-derived T-scores achieved an AUC of 0.959, κ = 0.74, and 90% accuracy. AI- and DXA-derived BMD showed comparable performance for predicting vertebral (AUC 0.704 vs. 0.678) and hip fractures (0.716 vs. 0.678). For all-site fractures, AI-derived BMD showed a modestly higher AUC than DXA-derived BMD (AUC, 0.699 vs 0.677; P = 0.042). Lower AI-derived BMD was independently associated with higher fracture risk.

Conclusions

AI-derived BMD from routine radiographs closely correlates with DXA and demonstrates comparable fracture prediction. This approach may support opportunistic osteoporosis screening without reliance on DXA.